Setsuo Arikawa

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By rapid progress of network and storage technologies, a huge amount of electronic data such as Web pages and XML data [23] has been available on intra and internet. These electronic data are heterogeneous collection of ill-structured data that have no rigid structures, and often called semi-structured data [1]. Hence, there have been increasing demands for(More)
We present a linear-time algorithm to compute the longest common prefix information in suffix arrays. As two applications of our algorithm, we show that our algorithm is crucial to the effective use of block-sorting compression, and we present a linear-time algorithm to simulate the bottom-up traversal of a suffix tree with a suffix array combined with the(More)
Arikawa, S., T. Shinohara and A. Yamamoto, Learning elementary formal systems, Theoretical Computer Science 95 (1992) 97-l 13. The elementary formal systems (EFS for short) Smullyan invented to develop his recursive function theory, are proved suitable to generate languages. In this paper we first point out that EFS can also work as a logic programming(More)
In this paper we address the problem of searching in LZW compressed text directly, and present a new algorithm for finding multiple patterns by simulating the move of the Aho-Corasick pattern matching machine. The new algorithm finds all occurrences of multiple patterns whereas the algorithm proposed by Amir, Benson, and Farach finds only the first(More)
We introduce a general framework which is suitable to capture an essence of compressed pattern matching according to various dictionary based compressions. The goal is to find all occurrences of a pattern in a text without decompression, which is one of the most active topics in string matching. Our framework includes such compression methods as Lempel-Ziv(More)
A scientist, whether theorist or experimenter, puts forward statements, or systems of statements, and test them step by step. In the field of the empirical sciences, more particularly, he constructs hypotheses, or systems of theories, and test them against experience by observation and experiment. Karl R. Popper: The Logic of Scientific Discovery 1 I n t r(More)
This paper describes a machine learning system that discovered a “negative motif”, in transmembrane domain identification from amino acid sequences, and reports its experiments on protein data using PIR database. We introduce a decision tree whose nodes are labeled with regular patterns. As a hypothesis, the system produces such a decision tree for a small(More)